Abstract

Phylogenetic footprinting is an approach to finding functionally important sequences
in the genome that relies on detecting their high degrees of conservation across different
species. A new study shows how much it improves the prediction of gene-regulatory
elements in the human genome.

Minireview

It has been a great challenge for biologists to understand the complicated and often
myriad mechanisms of gene regulation. The recent success of genome sequencing projects
[1,2], combined with very effective gene-prediction algorithms, has generated abundant
gene sequences, but our understanding of gene regulation has remained very limited.
In human and other higher eukaryotes, gene expression is modulated by the binding
of various transcription factors onto cis-regulatory regions of a gene. Binding of different combinations of transcription
factors may result in a gene being expressed in different tissue types or at different
developmental stages. To fully understand a gene's function, therefore, it is essential
to identify the transcription factors that regulate the gene and the corresponding
transcription-factor-binding sites (TFBSs) within the DNA sequence. Traditionally,
these regulatory sites were determined by labor-intensive wet-lab techniques such
as DNAse footprinting or gel-shift assays [3]; several online databases, such as TRRD, COMPEL and TRANSFAC [4,5] have been constructed to store experimentally determined TFBSs. Now, Lenhard and
colleagues [6] describe a new addition to the toolkit for TFBS prediction.

In recent years, various computational methods have been developed to model and predict
gene-regulatory elements. But predicting TFBSs has proved to be much harder than predicting
genes, the intrinsic difficulty being that TFBSs are in general very short and often
degenerate in sequence. Most TFBSs are short sequences of 6–12 base-pairs located
in the non-coding regions of a gene, most often in the 5' flanking region but sometimes
in the 3' region or even introns. Only between four and six bases within each TFBS
are fully conserved, however, with the other positions being highly variable from
gene to gene. As a result, TFBSs are often modeled using position-specific weight
matrices (PWMs) [7], which in essence summarize the relative frequencies of each of the four nucleotides
at each position. Figure 1 shows an example of such a matrix, for the human transcription factor GATA-1, from
the widely used TRANSFAC database [5].

Figure 1. An example of a position-specific weight matrix (PWM) adapted from the TRANSFAC database
[5]. The sequences that have been shown experimentally to bind to the human transcription
factor GATA-1 have 14 positions, among which only positions 6–10 are fully conserved.
Abbreviations: R, G or A (purine); N, any; S, G or C (strong); D, G or A or T. Twelve
sequences were used to build this matrix.

Given a PWM and a reliable scoring function, one can scan genomic DNA sequences and
identify potential TFBSs. But because TFBSs are highly degenerate, the majority of
predicted sites are 'false positives' that have no biological significance [8]. Several strategies have therefore been developed to reduce the false-positive rate;
these include combining predictions with gene-expression data [9] or using prior knowledge of gene co-regulation [10]. Another approach is to take advantage of the fact that genes are often regulated
by multiple transcription factors, so potential TFBSs tend to be clustered or adjacent
to each other [11]. Alternatively, some researchers have tried to create more precise and sensitive
tools for local sequence alignment and pattern discovery [12,13].

With the advance of genome sequencing projects, it has become obvious that comparing
genomic sequences across species – 'comparative genomics' – is a very effective way
to identify functionally important DNA sequences. At first comparative techniques
were primarily applied to the coding regions of genomes, to identify genes or exon-intron
boundaries [14]. More recently, such evolutionary approaches have become central to the efforts to
predict gene-regulatory sites, and the technique itself in this context has become
known as 'phylogenetic footprinting' [15,16], a term inspired by the wet-lab technique of DNAse footprinting. The reasoning behind
the approach is that, just like coding sequences, regulatory elements are functionally
important and are under evolutionary selection, so they should have evolved much more
slowly than other non-coding sequences. Genome-wide sequence comparison and studies
on individual genes have confirmed that regulatory elements are indeed conserved between
related species [17-19]. Thus, if we align the non-coding regions of orthologous genes from two species that
are sufficiently evolutionarily distant (but not too distant), we should be able to
detect the conserved regulatory elements interspersed between the truly non-functional
background sequences. This approach is illustrated schematically in Figure 2, in which a hypothetical human gene and its orthologs from mouse, rat and chimpanzee
are shown together; alignment of the orthologous sequences reveals conserved TFBSs
that are present in more than one species.

Figure 2. Using phylogenetic footprinting to detect conserved TFBSs. This schematic diagram
shows a hypothetical human gene aligned with its orthologs from three other mammals.
Cross-species sequence comparison reveals conserved TFBSs in each sequence. Sequence
motifs of the same shape (colored in green) represent binding-sites of the same class
of transcription factors. TFBS1 and TFBS4 are conserved in all four mammals; TFBS3
represents a newly acquired, primate-specific binding site. TFBS2 and TFBS2' represent
orthologous regulatory sites that have diverged significantly between the primate
and rodent lineages. Blue rectangles represent TATA boxes.

Phylogenetic footprinting was first performed by visually examining the alignment
of orthologous sequences; then, automated computer programs were developed to assist
the process. In this issue of Journal of Biology, Lenhard, Sandelin and colleagues describe their most recent success in predicting
TFBSs by comparative genome analysis [6]. They also introduce an interactive, web-based computational platform, ConSite [20], which allows users to do their own phylogenetic footprinting.

The power of any TFBS prediction algorithm that uses PWMs depends on the quality of
the matrix models that it uses, since the matrices represent an abstraction of experimentally
verified TFBSs. Lenhard and colleagues [6] collected TFBSs from both in vivo and in vitro assays and used an improved motif discovery algorithm, ANN-Spec [21], to construct over 100 distinct and high-quality TFBS profile matrices. These comprehensive
profiles were collected into an online database JASPAR [22], which is freely available to the scientific community. Users of ConSite can either
provide an existing alignment of two orthologous sequences or input just the sequences
alone and the program will generate the alignment. The program then scans the individual
sequences for potential TFBSs and compares the potential sites between the aligned
sequences. Only those conserved sites that are present in both sequences and also,
more importantly, are located in equivalent positions in the two aligned sequences,
are selected and reported in the output. The remainder of the sites, which are not
conserved between the two species, are considered to be false positives and are eliminated.

This phylogenetic filtering procedure significantly improves the power of TFBS prediction,
as is demonstrated by an example described in detail in the article by Lenhard et al. [6]. The authors compared the human β-globin promoter sequence with the orthologous sequences
from mouse and cow; this dramatically reduced the false-positive prediction of TFBSs
and they were able to identify a previously documented regulatory site. The authors
also studied a larger set of human-mouse gene pairs and compared the results predicted
by ConSite with the previously verified regulatory sites. On average, phylogenetic
footprinting improved the selectivity of TFBS prediction by 85% compared to using
matrix models alone, and could detect the majority of verified sites. When compared
with other available systems, ConSite has a flexible and easy-to-use web interface.
Users of the website can choose to search for binding sites for any numbers of transcription
factors or can even provide their own defined PWMs. The entire procedure and the output
graphs can be modulated by many user-specified parameters such as the extent of required
conservation (cut-off), and the length of sequence to search (window size).

It is becoming evident that comparative genome analysis is very powerful and will
be of use not only for genome annotation but also as an adjunct to more traditional
disciplines, such as molecular biology and genetics. Just like the sequence-alignment
programs that emerged in the early 1990s, ConSite and other similar programs [23,24] will prove very valuable and timely research tools for the scientific community.
Many new research directions are currently being pursued in this area; for example,
pair-wise sequence comparisons can be expanded to include multiple species and to
make use of additional information, such as evolutionary distance and phylogenetic
relationships [25]. More precise and effective sequence alignment programs have been created to handle
genome-scale sequences [26,27]. In addition to the human-mouse comparisons, some researchers are also proposing
cross-species comparison between human and other primates, which has been described
as 'phylogenetic shadowing' [28]. This approach complements human-rodent comparisons and will detect primate-specific
regulatory elements (see Figure 2). On the 'wet' experimental front, recent developments include microarray-based technologies
such as 'ChIP-chip', which combines chromatin immunoprecipitation (ChIP) with analysis
of the precipitated DNA on a microarray (chip), to detect TFBSs within a whole genome
[29]. It can be imagined that, with the emergence of more mammalian genome sequences in
the near future, we can finally identify all the gene regulatory elements in the human
genome and use them as a blueprint for understanding the mysteries of gene regulation.